def test_find_strength_diff(self):
bt = BradleyTerry()
bt.is_fitted = True
bt.target_col_name = 'result'
bt.lkp = {0: 'A', 1: 'B', 2: 'C', 3: 'D'}
bt.rplc_lkp = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
bt._params = np.array([0.3, 0.2, -0.2, -0.4])
bt.params_ = np.array([0.3, 0.2, -0.2, -0.4])
calc_strength = bt.find_strength_diff(INDEXED_DATA_NORESCOL)
correct_strength = np.array([-0.4, -0.1, 0.2, -0.2])
np.testing.assert_array_almost_equal(calc_strength,
correct_strength,
decimal=10)
def test_check_for_no_new_entities(self):
bt = BradleyTerry()
bt.is_fitted = True
bt.target_col_name = 'result'
bt.lkp = {1: 'A', 2: 'B', 3: 'C', 4: 'D'}
wrong_data = pd.DataFrame({
'ent1': ['K', 'A'],
'ent2': ['A,', 'B'],
'result': [1, 0]
})
wrong_data = wrong_data.set_index(['ent1', 'ent2'])
with self.assertRaises(Exception):
bt.check_for_no_new_entities(wrong_data)
try:
bt.check_for_no_new_entities(TRANSITIVE_DATA_INDEXED)
except:
self.fail("check_for_no_new_entities failed unexpectedly")
def test_predict_proba(self):
bt = BradleyTerry()
bt.is_fitted = True
bt.target_col_name = 'result'
bt.lkp = {0: 'A', 1: 'B', 2: 'C', 3: 'D'}
bt.rplc_lkp = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
bt._params = np.array([0.3, 0.2, -0.2, -0.4])
bt.params_ = np.array([0.3, 0.2, -0.2, -0.4])
bt.pylogit_fit = False
def exp_func(x):
return 1 / (1 + np.exp(-x))
pred_probs = bt.predict_proba(INDEXED_DATA_NORESCOL)
corect_probs = np.array(
[exp_func(-0.4),
exp_func(-0.1),
exp_func(0.2),
exp_func(-0.2)])
np.testing.assert_array_equal(pred_probs, corect_probs)